I agree with you this is an acceptable method if time spent on exporting data
from RDBM, importing file into HDFS and then importing data into RDBM again is
considered as well, but this is an single-process/thread method. BTW, can you
tell me how long does it take your method to process those 130 million rows,
how much is the data volume, and how powerful are your physical computers,
thanks a lot!
--------------------------------------------------
From: "Michael Lee" <[EMAIL PROTECTED]>
Sent: Thursday, July 24, 2008 11:51 AM
To: <core-user@hadoop.apache.org>
Subject: Re: Using MapReduce to do table comparing.
> Amber wrote:
>> We have a 10 million row table exported from AS400 mainframe every day, the
>> table is exported as a csv text file, which is about 30GB in size, then the
>> csv file is imported into a RDBMS table which is dropped and recreated every
>> day. Now we want to find how many rows are updated during each export-import
>> interval, the table has a primary key, so deletes and inserts can be found
>> using RDBMS joins quickly, but we must do a column to column comparing in
>> order to find the difference between rows ( about 90%) with the same primary
>> keys. Our goal is to find a comparing process which takes no more than 10
>> minutes with a 4-node cluster, each server in which has 4 4-core 3.0 GHz
>> CPUs, 8GB memory and a 300G local RAID5 array.
>>
>> Bellow is our current solution:
>> The old data is kept in the RDBMS with index created on the primary key,
>> the new data is imported into HDFS as the input file of our Map-Reduce job.
>> Every map task connects to the RDBMS database, and selects old data from it
>> for every row, map tasks will generate outputs if differences are found, and
>> there are no reduce tasks.
>>
>> As you can see, with the number of concurrent map tasks increasing, the
>> RDBMS database will become the bottleneck, so we want to kick off the RDBMS,
>> but we have no idea about how to retrieve the old row with a given key
>> quickly from HDFS files, any suggestion is welcome.
> 10 million is not bad. I do this all the time in UDB 8.1 - multiple key
> columns and multiple value columns and calculate delta's - insert,
> delete and update.
>
> What other has suggested works ( I tried very crude version of what
> James Moore suggested in Hadoop with 70+ million records ) but you have
> to remember there are other costs ( dumping out files, putting into
> HDFS, etc. ). It might be better if you process straight in database or
> do a straight file processing. Also the key is avoiding transaction.
>
> If you are doing outside of database...
>
> you have 'old.csv' and 'new.csv' and sorted by primary keys ( when you
> extract make sure you do order by ). In your application, you open two
> file handlers and read one line at time. Create the keys. If the keys
> are the same, you compare two strings if they are the same. If key is
> not the same, you have to find out natural orders - it can be insert or
> delete. Once you decide, you read another line ( if insert/delete - you
> only read one line from one of the file )
>
> Here is the pseudo code
>
> oldFile = File.new(oldFilename, "r")
> newFile = File.new(newFilename, "r")
> outFile = File.new(outFilename, "w")
>
> oldLine = oldFile.gets
> newLine = newFile.gets
>
> while ( true )
> {
> oldKey = convertToKey(oldLine)
> newKey = convertToKey(newLine)
>
> if ( oldKey < newKey )
> {
> ## it is deletion
> outFile.puts oldLine + "," + "DELETE";
> oldLine = oldFile.gets
> }
> elsif ( oldKey > newKey )
> {
> ## it is insert
> outFile.puts newLine + "," + "INSERT";
> newLine = newFile.gets
> }
> else
> {
> ## compare
> outFile.puts newLine + "," + "UPDATE" if ( oldLine != newLine )
>
> oldLine = oldFile.gets
> newLine = newFile.gets
> }
> }
>
> Okay - I skipped the part if eof is reached for each file but you get
> the point.
>
> If the both old and new are in database, you can open two databases
> connections and just do the process without dumping files.
>
> I journal about 130 million rows every day for quant financial database...
>
>
>
>
>
>
